Getting to Grips with Machine Learning
A new Machine Learning Initiative’s been launched by Dr. Marc Deisenroth from the Department of Computing at Imperial College London this month. It aims to bring together various machine learning researchers to provide a collaborative workspace suitable for researching, teaching, and learning of this fast-moving field.
A new Machine Learning Initiative’s been launched by Dr. Marc Deisenroth from the Department of Computing at Imperial College London this month. It aims to bring together various machine learning researchers to provide a collaborative workspace suitable for researching, teaching, and learning of this fast-moving field.
Machine learning is the driving force behind artificial intelligence (AI). It’s where algorithms and methodologies enable computers to learn on their own and improve on their own without the need for any further human intervention and without being explicitly programmed. It works by finding patterns in data that humans find difficult to process and makes predictions and decisions based on it.
Today, examples of machine learning can be seen all around us. It’s the very same technology that makes both Siri and Alexa work. Machine learning algorithms also power Google search results as well as recommend personalized products to us. Within the home, machine learning algorithms enable smart appliances to adjust themselves. While many cells phones today use faced detection using the same techniques. These are just a few examples, but as you can see, machine learning is quickly getting everywhere.
One area of machine learning that the College will be focusing on as part of the study is probabilistic modeling. The researchers will use probabilistic models in which to make predictions in a number of different areas. Reinforcement learning is another area of focus and is about using trial and error in which to learn. At Imperial, the researchers develop reinforcement learning algorithms that can be used in various applications across the robotics or healthcare industries. Imperial is hoping that the initiative will be a huge success and push the college into becoming a global player in machine learning research.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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